About Me

I am the CTO of Think Therapeutics. Previously, I was a graduate student at the MIT Computer Science & Artificial Intelligence Laboratory (CSAIL) focused on machine learning. I was advised by Prof. David Gifford, developing interpretability methods for understanding deep neural networks and designing therapeutics using ML.

I completed my undergrad and Masters at MIT, double majoring in computer science and mathematics. I also minored in economics. I graduated in June 2017 (undergrad) and June 2019 (MEng), advised by Prof. David Gifford.

My main interests broadly span machine learning, particularly as applied in computational biology and immunology. I am also interested in applications in natural language processing and computer vision.

I have had the pleasure to work at Google Brain, Facebook, Bloomberg LP, KAYAK, and Leiden University.

I am originally from Long Island, New York. In my free time I enjoy sailing, skiing, and flying.


Maximum n-times Coverage for COVID-19 Vaccine Design
Ge Liu, Brandon Carter, David Gifford
arXiv preprint: 2101.10902, 2021

Predicted Cellular Immunity Population Coverage Gaps for SARS-CoV-2 Subunit Vaccines and their Augmentation by Compact Peptide Sets
Ge Liu, Brandon Carter, David Gifford
Cell Systems, 2021
[Press] [Code]

Machine learning optimization of MHC class II presented peptides
Zheng Dai*, Brooke Huisman*, Haoyang Zeng, Brandon Carter, Siddhartha Jain, Michael Birnbaum, David Gifford
Bioinformatics, 2021
[Featured as spotlight talk at MLCB 2019]

Lost in Pruning: The Effects of Pruning Neural Networks beyond Test Accuracy
Lucas Liebenwein, Cenk Baykal, Brandon Carter, David Gifford, Daniela Rus
Machine Learning and Systems (MLSys), 2021

Computationally Optimized SARS-CoV-2 MHC Class I and II Vaccine Formulations Predicted to Target Human Haplotype Distributions
Ge Liu*, Brandon Carter*, Trenton Bricken, Siddhartha Jain, Mathias Viard, Mary Carrington, David Gifford
Cell Systems, 2020
[Press] [Code]

Overinterpretation reveals image classification model pathologies
Brandon Carter, Siddhartha Jain, Jonas Mueller, David Gifford
arXiv preprint: 2003.08907, 2020

Antibody complementarity determining region design using high-capacity machine learning
Ge Liu*, Haoyang Zeng*, Jonas Mueller, Brandon Carter, Ziheng Wang, Jonas Schilz, Geraldine Horny, Michael Birnbaum, Stefan Ewert, David Gifford
Bioinformatics, 2020

What made you do this? Understanding black-box decisions with sufficient input subsets
Brandon Carter*, Jonas Mueller*, Siddhartha Jain, David Gifford
Artificial Intelligence and Statistics (AISTATS), 2019
[Featured as contributed talk at NeurIPS 2018 Workshop on Interpretability and Robustness] [Slides] [Lecture notes] [Code]

Embedding Comparator: Visualizing Differences in Global Structure and Local Neighborhoods via Small Multiples
Angie Boggust*, Brandon Carter*, Arvind Satyanarayan
arXiv preprint: 1912.04853, 2019
[Demo] [Code]

Critiquing Protein Family Classification Models Using Sufficient Input Subsets
Brandon Carter, Maxwell Bileschi, Jamie Smith, Theo Sanderson, Drew Bryant, David Belanger, Lucy Colwell
Journal of Computational Biology, 2019
[Featured as spotlight talk at ICML 2019 Workshop on Computational Biology] [Slides]

Using Deep Learning to Classify the Protein Universe
Maxwell L Bileschi, David Belanger, Drew H Bryant, Theo Sanderson, Brandon Carter, D Sculley, Mark DePristo, Lucy Colwell
bioRxiv: 626507, 2019

Survey of Fully Verifiable Voting Cryptoschemes
Brandon Carter, Kenneth Leidal, Devin Neal, Zachary Neely
MIT Computer and Network Security (6.857) Final Project, 2016

Safety and Efficacy of Ganciclovir Ophthalmic Gel for Treatment of Adenovirus Keratoconjunctivitis Utilizing Cell Culture and Animal Models
Seth Epstein, Karen Fernandez, Brandon Carter, Salma Abdou, Neha Gadaria, Penny Asbell
Investigative Ophthalmology and Visual Science (IOVS), 2012

Interpreting Black-Box Models Through Sufficient Input Subsets
Brandon Carter
M.Eng Thesis, MIT Dept. of Electrical Engineering and Computer Science, 2019

* Equal Contribution

Full listing in Google Scholar.


Click on any of the projects below to learn more. You can also take a look at some of the contributions I have made on GitHub.

Twitter NLP Twitter NLP Follower Prediction
ICU Patient Predictions ICU Patient Predictions
Academics for the Future of Science Academics for the Future of Science
Ploegh Lab Website Ploegh Lab Website
StudentsThink StudentsThink


My email is bcarter [at] csail [dot] mit [dot] edu. Feel free to also connect with me on LinkedIn.